README.md

---
license: mit
task_categories:
- feature-extraction
- question-answering
language:
- en
tags:
- code
pretty_name: DeepScholarBench Dataset
size_categories:
- 1K<n<10K
configs:
- config_name: papers
  data_files: "papers_with_related_works.csv"
- config_name: citations
  data_files: "recovered_citations.csv" 
- config_name: important_citations
  data_files: "important_citations.csv"
- config_name: full
  data_files: ["papers_with_related_works.csv", "recovered_citations.csv", "important_citations.csv"]
---
# DeepScholarBench Dataset

[![Dataset](https://img.shields.io/badge/Dataset-deepscholar--bench%2FDeepScholarBench-blue)](https://huggingface.co/datasets/deepscholar-bench/DeepScholarBench)
[![GitHub](https://img.shields.io/badge/GitHub-deepscholar--bench-green)](https://github.com/guestrin-lab/deepscholar-bench)
[![License](https://img.shields.io/badge/License-MIT-yellow)](https://github.com/guestrin-lab/deepscholar-bench/blob/main/LICENSE)
[![Paper](https://img.shields.io/badge/Paper-arXiv%3A2508.20033-red)](https://arxiv.org/abs/2508.20033)
[![Leaderboard](https://img.shields.io/badge/Leaderboard-DeepScholar%20Bench-orange)](https://guestrin-lab.github.io/deepscholar-leaderboard/leaderboard/deepscholar_bench_leaderboard.html)

---

A comprehensive dataset of academic papers with extracted related works sections and recovered citations, designed for training and evaluating research generation systems.

## 📊 Dataset Overview

This dataset contains **63 academic papers** from ArXiv with their related works sections and **1630 recovered citations**, providing a rich resource for research generation and citation analysis tasks.

### 🎯 Use Cases

- **Research Generation**: Train models to generate related works sections
- **Citation Analysis**: Study citation patterns and relationships
- **Academic NLP**: Develop tools for academic text processing
- **Evaluation**: Benchmark research generation systems
- **Knowledge Discovery**: Analyze research trends and connections

## 📁 Dataset Structure

### 1. `papers_with_related_works.csv` (63 papers)

Contains academic papers with extracted related works sections in multiple formats:
| Column | Description |
|--------|-------------|
| `arxiv_id` | ArXiv identifier (e.g., "2506.02838v1") |
| `title` | Paper title |
| `authors` | Author names |
| `abstract` | Paper abstract |
| `categories` | ArXiv categories (e.g., "cs.AI, econ.GN") |
| `published_date` | Publication date |
| `updated_date` | Last update date |
| `abs_url` | ArXiv abstract URL |
| `arxiv_link` | Full ArXiv link |
| `publication_date` | Publication date |
| `raw_latex_related_works` | Raw LaTeX related works section |
| `clean_latex_related_works` | Cleaned LaTeX related works section |
| `pdf_related_works` | Related works extracted from PDF |

### 2. `recovered_citations.csv` (1630 citations)

Contains individual citations with recovered metadata:

| Column | Description |
|--------|-------------|
| `parent_paper_title` | Title of the paper containing the citation |
| `parent_paper_arxiv_id` | ArXiv ID of the parent paper |
| `citation_shorthand` | Citation key (e.g., "NBERw21340") |
| `raw_citation_text` | Raw citation text from LaTeX |
| `cited_paper_title` | Title of the cited paper |
| `cited_paper_arxiv_link` | ArXiv link if available |
| `cited_paper_abstract` | Abstract of the cited paper |
| `has_metadata` | Whether metadata was successfully recovered |
| `is_arxiv_paper` | Whether the cited paper is from ArXiv |
| `bib_paper_authors` | Authors of the cited paper |
| `bib_paper_year` | Publication year |
| `bib_paper_month` | Publication month |
| `bib_paper_url` | URL of the cited paper |
| `bib_paper_doi` | DOI of the cited paper |
| `bib_paper_journal` | Journal name |
| `original_title` | Original title from citation metadata |
| `search_res_title` | Title from search results |
| `search_res_url` | URL from search results |
| `search_res_content` | Content snippet from search results |

### 3. `important_citations.csv` (1,050 citations)

Contains enhanced citations with full paper metadata and content:

| Column | Description |
|--------|-------------|
| `parent_paper_title` | Title of the paper containing the citation |
| `parent_paper_arxiv_id` | ArXiv ID of the parent paper |
| `citation_shorthand` | Citation key (e.g., "NBERw21340") |
| `raw_citation_text` | Raw citation text from LaTeX |
| `cited_paper_title` | Title of the cited paper |
| `cited_paper_arxiv_link` | ArXiv link if available |
| `cited_paper_abstract` | Abstract of the cited paper |
| `has_metadata` | Whether metadata was successfully recovered |
| `is_arxiv_paper` | Whether the cited paper is from ArXiv |
| `cited_paper_authors` | Authors of the cited paper |
| `bib_paper_year` | Publication year |
| `bib_paper_month` | Publication month |
| `bib_paper_url` | URL of the cited paper |
| `bib_paper_doi` | DOI of the cited paper |
| `bib_paper_journal` | Journal name |
| `original_title` | Original title from citation metadata |
| `search_res_title` | Title from search results |
| `search_res_url` | URL from search results |
| `search_res_content` | Content snippet from search results |
| `arxiv_id` | ArXiv ID of the parent paper |
| `arxiv_link` | ArXiv link of the parent paper |
| `publication_date` | Publication date of the parent paper |
| `title` | Title of the parent paper |
| `abstract` | Abstract of the parent paper |
| `raw_latex_related_works` | Raw LaTeX related works section |
| `related_work_section` | Processed related works section |
| `pdf_related_works` | Related works extracted from PDF |
| `cited_paper_content` | Full content of the cited paper |

## ⚙️ Dataset Configurations

| Configuration | Description | Files | Records | Use Case |
|---------------|-------------|--------|---------|----------|
| `papers` | Academic papers only | `papers_with_related_works.csv` | 63 papers | Research generation, content analysis |
| `citations` | Citations only | `recovered_citations.csv` | 1,630 citations | Citation analysis, relationship mapping |
| `important_citations` | Enhanced citations with metadata | `important_citations.csv` | 1,050 citations | Advanced citation analysis, paper-citation linking |

## 🚀 Quick Start

### Loading from Hugging Face Hub (Recommended)

```python
from datasets import load_dataset

# Load papers dataset
papers = load_dataset("deepscholar-bench/DeepScholarBench", name="papers")["train"]
print(f"Loaded {len(papers)} papers")

# Load citations dataset  
citations = load_dataset("deepscholar-bench/DeepScholarBench", name="citations")["train"]
print(f"Loaded {len(citations)} citations")

# Load important citations with enhanced metadata
important_citations = load_dataset("deepscholar-bench/DeepScholarBench", name="important_citations")["train"]
print(f"Loaded {len(important_citations)} important citations")

# Convert to pandas for analysis
papers_df = papers.to_pandas()
citations_df = citations.to_pandas()
important_citations_df = important_citations.to_pandas()
```

### Example: Extract Related Works for a Paper

```python
# Get a specific paper
paper = papers_df[papers_df['arxiv_id'] == '2506.02838v1'].iloc[0]
print(f"Title: {paper['title']}")
print(f"Related Works:\n{paper['clean_latex_related_works']}")

# Get all citations for this paper
paper_citations = citations_df[citations_df['parent_paper_arxiv_id'] == '2506.02838v1']
print(f"Number of citations: {len(paper_citations)}")
```

### Example: Working with Important Citations

```python
# Load important citations (enhanced with paper metadata)
important_citations = load_dataset("deepscholar-bench/DeepScholarBench", name="important_citations")["train"]

# This configuration includes both citation data AND the parent paper information
sample = important_citations[0]
print(f"Citation: {sample['cited_paper_title']}")
print(f"Parent Paper: {sample['title']}")
print(f"Paper Abstract: {sample['abstract'][:200]}...")
print(f"Related Work Section: {sample['related_work_section'][:200]}...")

# Analyze citation patterns
important_df = important_citations.to_pandas()
print(f"Citations with full paper content: {important_df['cited_paper_content'].notna().sum()}")
print(f"Citations with related work sections: {important_df['related_work_section'].notna().sum()}")
```

## 📈 Dataset Statistics

- **Total Papers**: 63
- **Total Citations**: 1,630
- **Important Citations**: 1,050
- **Date Range**: 2024-2025 (recent papers)

## 🔧 Data Collection Process

This dataset was created using the [DeepScholarBench](https://github.com/guestrin-lab/deepscholar-bench) pipeline:

1. **ArXiv Scraping**: Collected papers by category and date range
2. **Author Filtering**: Focused on high-impact researchers (h-index ≥ 25)
3. **LaTeX Extraction**: Extracted related works sections from LaTeX source
4. **Citation Recovery**: Resolved citations and recovered metadata
5. **Quality Filtering**: Ensured data quality and completeness

## 📚 Related Resources

- **[GitHub Repository](https://github.com/guestrin-lab/deepscholar-bench)**: Full source code and documentation
- **[Data Pipeline](https://github.com/guestrin-lab/deepscholar-bench/tree/main/data_pipeline)**: Tools for collecting similar datasets
- **[Evaluation Framework](https://github.com/guestrin-lab/deepscholar-bench/tree/main/eval)**: Framework for evaluating research generation systems

## 🏆 Leaderboard

We maintain a leaderboard to track the performance of various models on the DeepScholarBench evaluation tasks:

- **[Official Leaderboard](https://guestrin-lab.github.io/deepscholar-leaderboard/leaderboard/deepscholar_bench_leaderboard.html)**: Live rankings of model performance
- **Evaluation Metrics**: Models are evaluated on relevance, coverage, and citation accuracy as described in the [evaluation guide](https://github.com/guestrin-lab/deepscholar-bench/tree/main/eval)
- **Submission Process**: Submit your results via this [Form](https://docs.google.com/forms/d/e/1FAIpQLSeug4igDHhVUU3XnrUSeMVRUJFKlHP28i8fcBAu_LHCkqdV1g/viewform)


## 🤝 Contributing

We welcome contributions to improve this dataset! Please see the [main repository](https://github.com/guestrin-lab/deepscholar-bench) for contribution guidelines.

## 📄 License

This dataset is released under the MIT License. See the [LICENSE](https://github.com/guestrin-lab/deepscholar-bench/blob/main/LICENSE) file for details.

---

**Note**: This dataset is actively maintained and updated. Check the GitHub repository for the latest version and additional resources.